Precision epidemiology

Phylogenetic trees can be used to denote how pathogens isolated from different patients are related. This has proven to be an important tool to study the spread of diseases.Such phylogenetic trees can be reconstructed from genetic sequence data from pathogens by making use of the fact that pathogens isolated from patients further apart in the transmission history have genetic sequences with more nucleotide differences. Additionally, past population dynamics can be reconstructed from such phylogenetic trees by using phylodynamic methods. These approaches have been used to study the spread of various diseases, such as the 2009 influenza A/H1N1 pandemic, the 2014/15 Ebola outbreak in western Africa, ZIKA viruses in South America or MERS coronavirus in Saudi Arabia.In order to extract transmission patterns from phylogenetic trees, the shared evolutionary history of pathogens isolated from hosts has to actually be tree like. Recombination processes, however, cause this shared evolutionary history to be a network and not just a tree. Recombination processes can cause lineages to carry the genetic information of more than just one ancestral lineages. In order to study such processes, methods that are able to infer recombination networks (and not just trees) are required.One of these recombination process is called reassortment, where segments of segmented viruses are re-shuffled upon a co-infection event. In a recently introduced method, we showed that we are indeed able to infer such networks from full genomic sequences of human influenza viruses. This allowed us to infer where and when reassortment events occurred while properly accounting for uncertainty in the data and to show that lineages that persist for longer tend to have more such events. However, there remains a lot unknown about what are the determining factors that make reassortment events successful. In the first part of this research project, I will elucidate the factors that make reassortment events successful or not by studying the fitness effects of these events from reassortment networks of human influenza viruses.While existing for reassortment, inferring recombination networks remains challenging for other genetic recombination events. One of the most prevalent of those is homologous recombination.In the second part of this research project, I will extend the current Markov chain Monte Carlo approach to infer reassortment networks to allow for homologous recombination. This will allows us to study recombination processes across different pathogens. Additionally, it will allows of to perform phylogenetic and phylodynamic inferences for pathogens that recombine using full genomes. This in turn will greatly increase our ability to elucidate how pathogens spread.